DOCSLIB.ORG

Brief Report Loss of B Cell Anergy in Type 1 Diabetes Is Associated With

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Brief Report Loss of B Cell Anergy in Type 1 Diabetes Is Associated With Page 1 of 63 Diabetes Brief Report Loss of B cell anergy in type 1 diabetes is associated with high risk HLA and non- HLA disease susceptibility alleles Mia J. Smith1,2, Marynette Rihanek3, Clive Wasserfall4, Clayton E. Mathews4, Mark A. Atkinson4,5, Peter A. Gottlieb3, and John C. Cambier1 1Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, CO; 2Department of Microbiology, Immunology, and Pathology, Colorado State University, Fort Collins, CO; 3Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine, Aurora, CO; 4Department of Pathology, Immunology, and Laboratory Medicine, The University of Florida College of Medicine, Gainesville, FL; 5Department of Pediatrics, The University of Florida College of Medicine, Gainesville, FL Corresponding Author: John C. Cambier 12800 E 19th Avenue RC1 North, P18-8100 Aurora, CO 80045-2537 Phone: 303-724-8663 E-mail: [email protected] Condensed Title: Genetic associations with loss of B cell anergy in type 1 diabetes Word Count: 2557/2000, Abstract (202/200), Figures (3), Tables (0), References (37/25) Diabetes Publish Ahead of Print, published online January 17, 2018 Diabetes Page 2 of 63 Although B cells reactive with islet autoantigens are silenced by tolerance mechanisms in healthy individuals, they can become activated and contribute to development of type 1 diabetes. We previously demonstrated that high-affinity insulin-binding B cells (IBCs) occur exclusively in the anergic (BND) compartment in peripheral blood of healthy subjects. Consistent with their activation early in disease development, high-affinity IBCs are absent from the BND compartment of some first-degree relatives (FDRs) as well as all autoantibody positive pre- diabetic and new-onset type 1 diabetes patients, a time when they are found in pancreatic islets. Loss of BND IBCs is associated with loss of the entire BND B cell compartment, consistent with provocation by an environmental trigger or predisposing genetic factors. To investigate potential mechanisms operative in subversion of B cell tolerance, we explored associations between HLA and non- HLA type 1 diabetes-associated risk allele genotypes and loss of BNDs in FDRs. We found that high-risk HLA alleles and a subset of non-HLA risk alleles, i.e. PTPN2 (rs1893217), INS (rs689) and IKZF3 (rs2872507), relevant to B and T cell development and function, are associated with loss of anergy. Hence, our results suggest a role for risk-conferring alleles in perturbation of B cell anergy during development of type 1 diabetes. Page 3 of 63 Diabetes Type 1 diabetes (T1D) is an autoimmune disease in which self-reactive lymphocytes destroy insulin-producing pancreatic beta cells. While genetic variation is thought to be the major contributor to risk of developing T1D, environment also plays a contributing role. Together, these factors may impart their effects by compromising maintenance of immune tolerance in T cells and/or B cells, both of which are known to be essential in the pathogenesis of the disorder (1-4). Studies have shown that B cells likely act as antigen presenting cells and autoantibody producers in type 1 diabetes (5; 6). How self- reactive B cells, which are normally silenced in healthy individuals, become activated to participate in this disease is not known. Previous studies have demonstrated that up to 70% of all B cells generated in the bone marrow are autoreactive (7). Autoreactive B cells are silenced by multiple mechanisms. Those reactive with highly avid self-antigens, e.g. cell surface proteins, undergo receptor editing, in which they rearrange their antigen receptor light chains modifying specificity (8). If this process fails to eliminate autoreactivity, cells can undergo apoptosis via a mechanism referred to as clonal deletion (9). Cells reactive with low avidity autoantigens, even if they have high affinity, do not receive signals that are sufficiently strong to induce receptor editing or clonal deletion. These cells mature and proceed to the periphery where they are maintained in a state of unresponsiveness termed anergy. Anergic B cells show evidence of previous antigen exposure including downregulation of surface IgM, elevated basal calcium and activation of negatively regulating signaling circuitry, but are refractory to further stimulation (10-12). Importantly, studies in mice have demonstrated that anergy is rapidly reversed if autoantigen dissociates from the B cell receptor (BCR), suggesting that this Diabetes Page 4 of 63 unresponsive state is maintained by a non-durable, presumably fragile, biochemical mechanism, rather than genetic reprogramming (13). Consistent with this mechanism, inhibitory signaling pathways are upregulated in anergic cells by protein phosphorylation, e.g. SHIP1 and SHP-1, and miRNA regulation of effector expression, e.g. PTEN (14; 15). B cell intrinsic expression of these regulatory phosphatases is required for maintenance of anergy (14). It is likely that additional genetic factors play a role in tuning B cell responsiveness to antigen and maintenance of anergy. Obvious candidates reside among the products of gene alleles that have been shown to confer increased risk of developing autoimmunity. We previously examined the status of insulin-reactive B cells (IBC) in peripheral blood of healthy individuals. We observed that B cells with high-affinity for insulin occur in blood of healthy subjects where they are restricted in the anergic compartment (16). These cells are polyreactive, binding to LPS and chromatin, as well as insulin. Interestingly, they disappear from this compartment in islet autoantibody-positive and recent-onset type 1 diabetes subjects, as well as a portion of healthy first-degree relatives (FDRs) of type 1 diabetes patients (Figure 1 and Supplemental figure 1). Preliminary studies in our lab suggest the disappearance of these cells reflects their relocalization to the pancreas and pancreatic lymph nodes. Specifically, IBC are enriched among B cells in pancreatic islets of type 1 diabetics (Smith and Cambier, unpublished). However, we cannot rule out the possibilities that these cells simply upregulate surface IgM, and thus enter the mature naïve compartment, or do not enter the anergic compartment. Page 5 of 63 Diabetes To better understand what factors contribute to loss of B cells from the anergic compartment of blood early in type 1 diabetes, we explored the correlation between BND frequency among FDRs and high-risk HLA and non-HLA type 1 risk allele genotype. Diabetes Page 6 of 63 RESEARCH DESIGN AND METHODS Subject selection and peripheral blood processing Peripheral blood was obtained with informed consent at the Barbara Davis Center for Childhood Diabetes using protocols approved by the University of Colorado Institutional Review Board. Eligible type 1 diabetes subjects were males or females who met the American Diabetes Association criteria for classification of disease. Autoantibodies against insulin (IAA), GAD, IA-2, and ZnT8 were assayed using radioimmunoassays, as previously described (17). Peripheral blood mononuclear cells (PBMCs) from autoantibody negative FDR (n=103), autoantibody positive pre-diabetes subjects (n=18; identified in the Type 1 Diabetes TrialNet Natural History study), new-onset type 1 diabetes patients (n=21; duration < 12 months), long-standing type 1 diabetes patients (n=21; duration > 12 months), and healthy age/sex matched controls (n=49) were isolated from heparinized blood by Ficoll-Hypaque fractionation. DNA was extracted from the granulocyte layer using the DNA midi kit (Qiagen). Flow cytometric analysis and enrichment of IBCs In order to maintain consistency of gating of flow cytometry data, each subject sample set was analyzed in parallel with an age/sex matched healthy control. The healthy control cells were used to construct gates, which were then copied to the FDR subject cells. Gating for BND cells was based on CD19- T cells and FMO compensation controls. PBMCs were stained in PBS/1%BSA/0.02%NaAzide with human FcR Blocking Reagent (Miltenyi), 0.1 µg/106 cells insulin-biotin, and mouse monoclonal anti-human antibodies against CD19-BV510 (BioLegend), CD27-PerCP or CD27-BUV395 (BioLegend), IgM- Page 7 of 63 Diabetes PE (Southern Biotech), and IgD-FITC (BD Biosciences) or IgD-BV421 (BioLegend) for 20 minutes at 4ºC. After washing, cells were fixed with 2% formaldehyde at 4ºC, followed by incubation with streptavidin-AlexaFlour647 for 20 minutes at 4ºC. Cells were washed, suspended in MACS buffer (PBS/0.5%BSA/2mMEDTA), and incubated with Anti-Cy5/Anti-Alexa647 microbeads (Miltenyi) for 15 minutes at 4ºC. Samples were then passed over magnetized LS columns (Miltenyi), washed 3 times with 2 mL of MACS buffer, and bound cells were eluted with 6 mL of MACS buffer. Flow cytometry was preformed an LSR II (BD) and data analyzed with FlowJo software version 8.8.4. Absolute cell counts were performed using a hemocytometer (Fischer Scientific) to enumerate total lymphocytes. Total B cell numbers were then determined by multiplying the frequency of CD19+ B cells determined by FACS by the total cell count determined by the hemocytometer. Absolute IBC numbers were determined by multiplying the frequency of IBCs in the CD19+ B cell gate by the total B cell count. Absolute IBC BND cells were determined by multiplying the frequency of CD27- IgMlo/- IgD+ cells within the IBC gate by the total IBC cell count. HLA and non-HLA genotyping DNA from FDRs (n=103) and healthy controls (n=49) was HLA genotyped at the Autoantibody/HLA Service Center at the Barbara Davis Center for Childhood Diabetes using established protocols (18). DNA from 60 FDRs was sent to the University of Florida Center for Pharmacogenomics for non-HLA genotyping. The purified DNA was then genotyped on a custom Taqman single nucleotide polymorphism (SNP) genotyping Diabetes Page 8 of 63 array (ThermoFisher). All results from non-HLA genotyping are available in Supplemental Table 1. Statistics Data were analyzed using Prism software (GraphPad Software, Inc.). One-way ANOVA followed by a Bonferroni’s Multiple Comparison post-test was used to determine significance of differences along the five patient groups, defined as P<0.05.
Load more

Recommended publications
  • Single-Cell RNA Sequencing Demonstrates the Molecular and Cellular Reprogramming of Metastatic Lung Adenocarcinoma
    ARTICLE https://doi.org/10.1038/s41467-020-16164-1 OPEN Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma Nayoung Kim 1,2,3,13, Hong Kwan Kim4,13, Kyungjong Lee 5,13, Yourae Hong 1,6, Jong Ho Cho4, Jung Won Choi7, Jung-Il Lee7, Yeon-Lim Suh8,BoMiKu9, Hye Hyeon Eum 1,2,3, Soyean Choi 1, Yoon-La Choi6,10,11, Je-Gun Joung1, Woong-Yang Park 1,2,6, Hyun Ae Jung12, Jong-Mu Sun12, Se-Hoon Lee12, ✉ ✉ Jin Seok Ahn12, Keunchil Park12, Myung-Ju Ahn 12 & Hae-Ock Lee 1,2,3,6 1234567890():,; Advanced metastatic cancer poses utmost clinical challenges and may present molecular and cellular features distinct from an early-stage cancer. Herein, we present single-cell tran- scriptome profiling of metastatic lung adenocarcinoma, the most prevalent histological lung cancer type diagnosed at stage IV in over 40% of all cases. From 208,506 cells populating the normal tissues or early to metastatic stage cancer in 44 patients, we identify a cancer cell subtype deviating from the normal differentiation trajectory and dominating the metastatic stage. In all stages, the stromal and immune cell dynamics reveal ontological and functional changes that create a pro-tumoral and immunosuppressive microenvironment. Normal resident myeloid cell populations are gradually replaced with monocyte-derived macrophages and dendritic cells, along with T-cell exhaustion. This extensive single-cell analysis enhances our understanding of molecular and cellular dynamics in metastatic lung cancer and reveals potential diagnostic and therapeutic targets in cancer-microenvironment interactions. 1 Samsung Genome Institute, Samsung Medical Center, Seoul 06351, Korea.
    [Show full text]
  • Supplementary Table S5. Differentially Expressed Gene Lists of PD-1High CD39+ CD8 Tils According to 4-1BB Expression Compared to PD-1+ CD39- CD8 Tils
    BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) J Immunother Cancer Supplementary Table S5. Differentially expressed gene lists of PD-1high CD39+ CD8 TILs according to 4-1BB expression compared to PD-1+ CD39- CD8 TILs Up- or down- regulated genes in Up- or down- regulated genes Up- or down- regulated genes only PD-1high CD39+ CD8 TILs only in 4-1BBneg PD-1high CD39+ in 4-1BBpos PD-1high CD39+ CD8 compared to PD-1+ CD39- CD8 CD8 TILs compared to PD-1+ TILs compared to PD-1+ CD39- TILs CD39- CD8 TILs CD8 TILs IL7R KLRG1 TNFSF4 ENTPD1 DHRS3 LEF1 ITGA5 MKI67 PZP KLF3 RYR2 SIK1B ANK3 LYST PPP1R3B ETV1 ADAM28 H2AC13 CCR7 GFOD1 RASGRP2 ITGAX MAST4 RAD51AP1 MYO1E CLCF1 NEBL S1PR5 VCL MPP7 MS4A6A PHLDB1 GFPT2 TNF RPL3 SPRY4 VCAM1 B4GALT5 TIPARP TNS3 PDCD1 POLQ AKAP5 IL6ST LY9 PLXND1 PLEKHA1 NEU1 DGKH SPRY2 PLEKHG3 IKZF4 MTX3 PARK7 ATP8B4 SYT11 PTGER4 SORL1 RAB11FIP5 BRCA1 MAP4K3 NCR1 CCR4 S1PR1 PDE8A IFIT2 EPHA4 ARHGEF12 PAICS PELI2 LAT2 GPRASP1 TTN RPLP0 IL4I1 AUTS2 RPS3 CDCA3 NHS LONRF2 CDC42EP3 SLCO3A1 RRM2 ADAMTSL4 INPP5F ARHGAP31 ESCO2 ADRB2 CSF1 WDHD1 GOLIM4 CDK5RAP1 CD69 GLUL HJURP SHC4 GNLY TTC9 HELLS DPP4 IL23A PITPNC1 TOX ARHGEF9 EXO1 SLC4A4 CKAP4 CARMIL3 NHSL2 DZIP3 GINS1 FUT8 UBASH3B CDCA5 PDE7B SOGA1 CDC45 NR3C2 TRIB1 KIF14 TRAF5 LIMS1 PPP1R2C TNFRSF9 KLRC2 POLA1 CD80 ATP10D CDCA8 SETD7 IER2 PATL2 CCDC141 CD84 HSPA6 CYB561 MPHOSPH9 CLSPN KLRC1 PTMS SCML4 ZBTB10 CCL3 CA5B PIP5K1B WNT9A CCNH GEM IL18RAP GGH SARDH B3GNT7 C13orf46 SBF2 IKZF3 ZMAT1 TCF7 NECTIN1 H3C7 FOS PAG1 HECA SLC4A10 SLC35G2 PER1 P2RY1 NFKBIA WDR76 PLAUR KDM1A H1-5 TSHZ2 FAM102B HMMR GPR132 CCRL2 PARP8 A2M ST8SIA1 NUF2 IL5RA RBPMS UBE2T USP53 EEF1A1 PLAC8 LGR6 TMEM123 NEK2 SNAP47 PTGIS SH2B3 P2RY8 S100PBP PLEKHA7 CLNK CRIM1 MGAT5 YBX3 TP53INP1 DTL CFH FEZ1 MYB FRMD4B TSPAN5 STIL ITGA2 GOLGA6L10 MYBL2 AHI1 CAND2 GZMB RBPJ PELI1 HSPA1B KCNK5 GOLGA6L9 TICRR TPRG1 UBE2C AURKA Leem G, et al.
    [Show full text]
  • Cellular and Molecular Signatures in the Disease Tissue of Early
    Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of
    [Show full text]
  • Anti-SIRPG Monoclonal Antibody, Clone OX119 (CABT-53873MH) This Product Is for Research Use Only and Is Not Intended for Diagnostic Use
    Anti-SIRPG monoclonal antibody, clone OX119 (CABT-53873MH) This product is for research use only and is not intended for diagnostic use. PRODUCT INFORMATION Product Overview Mouse anti Human CD172g antibody, clone OX119 recognizes human SIRP gamma, a 55kD signal regulatory protein which is a member of the immunoglobulin gene superfamily. SIRP gamma, also known as CD172g, is expressed on most T lymphocytes and a subset of B-cells but is absent on myeloid cells. CD172g has a truncated cytoplasmic tail, which is similar to SIRP beta, but unlike SIRP beta CD172g does not require DAP12 for expression at the cell surface. CD172g, like SIRP alpha, binds to CD47 inducing apoptosis. It is also involved in the negative regulation of receptor tyrosine kinase-coupled signalling. Flow Cytometry Use 10ul of the suggested working dilution to label 1x106 cells in 100ul. Specificity SIRPG Immunogen Purified recombinant SIRPgamma.CD4. Isotype IgG1 Source/Host Mouse Species Reactivity Human Clone OX119 Conjugate Unconjugated Applications FC; IP Format Purified IgG - liquid Size 200 μg Preservative 0.09% Sodium Azide Storage in frost-free freezers is not recommended. This product should be stored undiluted. Avoid repeated freezing and thawing as this may denature the antibody. Should this product contain a precipitate we recommend microcentrifugation before use. Warnings For research purposes only 45-1 Ramsey Road, Shirley, NY 11967, USA Email: [email protected] Tel: 1-631-624-4882 Fax: 1-631-938-8221 1 © Creative Diagnostics All Rights Reserved
    [Show full text]
  • Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
    Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase
    [Show full text]
  • A Novel Foxp3-Related Immune Prognostic Signature for Glioblastoma Multiforme Based on Immunogenomic Profiling
    www.aging-us.com AGING 2021, Vol. 13, No. 3 Research Paper A novel foxp3-related immune prognostic signature for glioblastoma multiforme based on immunogenomic profiling Xiao-Yu Guo1,*, Guan-Hua Zhang1,2,*, Zhen-Ning Wang1, Hao Duan1, Tian Xie1, Lun Liang1, Rui Cui1, Hong-Rong Hu1, Yi Wu3, Jia-jun Dong3, Zhen-Qiang He1, Yong-Gao Mou1 1Department of Neurosurgery AND Neuro-Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China 2Department of Cerebrovascular Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510000, China 3Department of Neurosurgery, Jiangmen Central Hospital, Jiangmen 529030, China *Equal contribution Correspondence to: Yong-Gao Mou, Zhen-Qiang He; email: [email protected], [email protected] Keywords: glioblastoma multiforme, Foxp3, regulatory T cells, immune prognostic signature, nomogram Received: June 8, 2020 Accepted: October 31, 2020 Published: January 10, 2021 Copyright: © 2021 Guo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT Foxp3+ regulatory T cells (Treg) play an important part in the glioma immunosuppressive microenvironment. This study analyzed the effect of Foxsp3 on the immune microenvironment and constructed a Foxp3-related immune prognostic signature (IPS)for predicting prognosis in glioblastoma multiforme (GBM). Immunohistochemistry (IHC) staining for Foxp3 was performed in 72 high-grade glioma specimens. RNA-seq data from 152 GBM samples were obtained from The Cancer Genome Atlas database (TCGA) and divided into two groups, Foxp3 High (Foxp3_H) and Foxp3 Low (Foxp3_L), based on Foxp3 expression.
    [Show full text]
  • The Interleukin-18 Receptor Is Differentially Expressed in Whole
    1 The interleukin-18 receptor is differentially expressed in the blood during sepsis. 2 3 Shahan Mamoor, MS1 4 [email protected] East Islip, NY, USA 5 6 We probed published and public microarray datasets1,2 to discover the most significant gene expression changes in the blood of patients with sepsis. We found significant induction 7 expression of IL18RAP and IL18R1, genes encoding subunits of the interleukin-18 receptor, in 8 whole blood from patients with sepsis. 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Keywords: sepsis, septic shock, interleukin-18, IL18R1, IL18RAP, systems biology of septic 26 shock. 27 28 PAGE 1 OF 13 1 Septic shock is a leading cause of mortality in the United States and worldwide3. We 2 used published and public microarray datasets1,2 to identify differentially expressed genes in the 3 4 blood of patients with sepsis. We identified IL18R1 and IL18RAP as among the genes most 5 differentially expressed in blood in the septic state. 6 7 Methods 8 9 We utilized microarray datasets GSE1001591 and GSE264402 for this differential gene 10 11 expression analysis of blood cells during sepsis. GSE100509 was generated with whole blood 12 using Illumina HumanWG-6 v3.0 expression beadchip technology with n=12 whole blood from 13 control subjects and n=33 whole blood from sepsis patients. GSE26440 was generated using 14 15 Affymetrix Human Genome U133 Plus 2.0 Array technology with n=32 control subjects and 16 n=98 sepsis patients. The Benjamini and Hochberg method of p-value adjustment was used for 17 ranking of differential expression but raw p-values were used for assessment of statistical 18 19 significance of global differential expression.
    [Show full text]
  • Supplementary Material DNA Methylation in Inflammatory Pathways Modifies the Association Between BMI and Adult-Onset Non- Atopic
    Supplementary Material DNA Methylation in Inflammatory Pathways Modifies the Association between BMI and Adult-Onset Non- Atopic Asthma Ayoung Jeong 1,2, Medea Imboden 1,2, Akram Ghantous 3, Alexei Novoloaca 3, Anne-Elie Carsin 4,5,6, Manolis Kogevinas 4,5,6, Christian Schindler 1,2, Gianfranco Lovison 7, Zdenko Herceg 3, Cyrille Cuenin 3, Roel Vermeulen 8, Deborah Jarvis 9, André F. S. Amaral 9, Florian Kronenberg 10, Paolo Vineis 11,12 and Nicole Probst-Hensch 1,2,* 1 Swiss Tropical and Public Health Institute, 4051 Basel, Switzerland; [email protected] (A.J.); [email protected] (M.I.); [email protected] (C.S.) 2 Department of Public Health, University of Basel, 4001 Basel, Switzerland 3 International Agency for Research on Cancer, 69372 Lyon, France; [email protected] (A.G.); [email protected] (A.N.); [email protected] (Z.H.); [email protected] (C.C.) 4 ISGlobal, Barcelona Institute for Global Health, 08003 Barcelona, Spain; [email protected] (A.-E.C.); [email protected] (M.K.) 5 Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain 6 CIBER Epidemiología y Salud Pública (CIBERESP), 08005 Barcelona, Spain 7 Department of Economics, Business and Statistics, University of Palermo, 90128 Palermo, Italy; [email protected] 8 Environmental Epidemiology Division, Utrecht University, Institute for Risk Assessment Sciences, 3584CM Utrecht, Netherlands; [email protected] 9 Population Health and Occupational Disease, National Heart and Lung Institute, Imperial College, SW3 6LR London, UK; [email protected] (D.J.); [email protected] (A.F.S.A.) 10 Division of Genetic Epidemiology, Medical University of Innsbruck, 6020 Innsbruck, Austria; [email protected] 11 MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, W2 1PG London, UK; [email protected] 12 Italian Institute for Genomic Medicine (IIGM), 10126 Turin, Italy * Correspondence: [email protected]; Tel.: +41-61-284-8378 Int.
    [Show full text]
  • Conserved Transcriptomic Profile Between Mouse and Human Colitis
    ARTICLE https://doi.org/10.1038/s41467-019-10769-x OPEN Conserved transcriptomic profile between mouse and human colitis allows unsupervised patient stratification Paulo Czarnewski1, Sara M. Parigi1, Chiara Sorini 1, Oscar E. Diaz 1, Srustidhar Das1, Nicola Gagliani1,2,3 & Eduardo J. Villablanca 1,3 1234567890():,; Clinical manifestations and response to therapies in ulcerative colitis (UC) are hetero- geneous, yet patient classification criteria for tailored therapies are currently lacking. Here, we present an unsupervised molecular classification of UC patients, concordant with response to therapy in independent retrospective cohorts. We show that classical clustering of UC patient tissue transcriptomic data sets does not identify clinically relevant profiles, likely due to associated covariates. To overcome this, we compare cross-sectional human data sets with a newly generated longitudinal transcriptome profile of murine DSS-induced colitis. We show that the majority of colitis risk-associated gene expression peaks during the inflammatory rather than the recovery phase. Moreover, we achieve UC patient clustering into two distinct transcriptomic profiles, differing in neutrophil-related gene activation. Notably, 87% of patients in UC1 cluster are unresponsive to two most widely used biological therapies. These results demonstrate that cross-species comparison enables stratification of patients undistinguishable by other molecular approaches. 1 Immunology and Allergy Unit, Department of Medicine, Solna, Karolinska Institute and University Hospital, 17176 Stockholm, Sweden. 2 Department of Medicine and Department of General, Visceral and Thoracic Surgery, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany. 3These authors jointly supervised this work: Nicola Gagliani, Eduardo J. Villablanca. Correspondence and requests for materials should be addressed to E.J.V.
    [Show full text]
  • Cancer-Specific Immune Prognostic Signature in Solid Tumors and Its Relation to Immune Checkpoint Therapies
    Cancers 2020, 12 S1 of S8 Supplementary Materials: Cancer-Specific Immune Prognostic Signature in Solid Tumors and Its Relation to Immune Checkpoint Therapies Shaoli Das, Kevin Camphausen and Uma Shankavaram Supplementary Figures Figure S1. ssGSEA of scRNA-seq data of tumor samples from four solid tumor histologies (breast, colon, glioblastoma, and head and neck tumors) identifies three or two dominant immune-function- associated cell clusters. One of these clusters is enriched for monocytes, macrophages, dendritic cells (MoMaDC), another cluster is enriched for NK-, T-cells, and another is enriched for B-cell functions. For head and neck tumors and glioblastomas, B-cell enrichments does not form a distinct cluster but forms a unified cluster along with NK- and T-cells. (a) Analysis of a breast cancer scRNA-seq data set identifies nine cell clusters from unsupervised t-SNE clustering. (b) These clusters were assigned to three distinct sets of immune functions using ssGSEA, as seen from the heat map (one related to monocytes, macrophages, TLR and dendritic cells; one related to B-cell functions; and the other related to NK- and T-cell functions). (c) The stacked bar plot shows that cell clusters 1, 3, and 4 are enriched for functions related to monocytes, macrophages, and dendritic cells; the cell cluster 6 is enriched for B cell functions; and cell cluster 7 is enriched for NK- and T- related functions. (d) Analysis of a GBM scRNA-seq data set identifies 10 cell clusters from unsupervised t-SNE clustering. (e) These clusters were assigned to two distinct sets of immune functions using ssGSEA, as seen from the heat map (one related to monocytes, macrophages, TLR, dendritic cells and the other related to NK-, T- and B-cell functions).
    [Show full text]
  • Supplementary Table 1
    Supplementary Table 1. 492 genes are unique to 0 h post-heat timepoint. The name, p-value, fold change, location and family of each gene are indicated. Genes were filtered for an absolute value log2 ration 1.5 and a significance value of p ≤ 0.05. Symbol p-value Log Gene Name Location Family Ratio ABCA13 1.87E-02 3.292 ATP-binding cassette, sub-family unknown transporter A (ABC1), member 13 ABCB1 1.93E-02 −1.819 ATP-binding cassette, sub-family Plasma transporter B (MDR/TAP), member 1 Membrane ABCC3 2.83E-02 2.016 ATP-binding cassette, sub-family Plasma transporter C (CFTR/MRP), member 3 Membrane ABHD6 7.79E-03 −2.717 abhydrolase domain containing 6 Cytoplasm enzyme ACAT1 4.10E-02 3.009 acetyl-CoA acetyltransferase 1 Cytoplasm enzyme ACBD4 2.66E-03 1.722 acyl-CoA binding domain unknown other containing 4 ACSL5 1.86E-02 −2.876 acyl-CoA synthetase long-chain Cytoplasm enzyme family member 5 ADAM23 3.33E-02 −3.008 ADAM metallopeptidase domain Plasma peptidase 23 Membrane ADAM29 5.58E-03 3.463 ADAM metallopeptidase domain Plasma peptidase 29 Membrane ADAMTS17 2.67E-04 3.051 ADAM metallopeptidase with Extracellular other thrombospondin type 1 motif, 17 Space ADCYAP1R1 1.20E-02 1.848 adenylate cyclase activating Plasma G-protein polypeptide 1 (pituitary) receptor Membrane coupled type I receptor ADH6 (includes 4.02E-02 −1.845 alcohol dehydrogenase 6 (class Cytoplasm enzyme EG:130) V) AHSA2 1.54E-04 −1.6 AHA1, activator of heat shock unknown other 90kDa protein ATPase homolog 2 (yeast) AK5 3.32E-02 1.658 adenylate kinase 5 Cytoplasm kinase AK7
    [Show full text]
  • An Autoimmune Disease Risk SNP, Rs2281808, in SIRPG Is Associated
    www.nature.com/scientificreports OPEN An autoimmune disease risk SNP, rs2281808, in SIRPG is associated with reduced expression of SIRPγ Received: 29 June 2018 Accepted: 8 October 2018 and heightened efector state in Published: xx xx xxxx human CD8 T-cells Sushmita Sinha1, Nicholas Borcherding1, Pranav S. Renavikar1, Michael P. Crawford1, Eva Tsalikian2, Michael Tansey2, Ezzatollah T. Shivapour3, Frank Bittner3, John Kamholz3, Heena Olalde3, Emilee Gibson3 & Nitin J. Karandikar1 Multiple GWAS studies have shown that the SNP rs2281808 TT variant, present within the SIRPG gene, is associated with autoimmune diseases, such as type 1 diabetes. However, the role of SIRPγ in human T-cells is not known, neither is the functional signifcance of TT variant. Here we investigated SIRPG genotypes and their efects on the fate and function of human T-cells. We found that the presence of T variant resulted in reduction of SIRPγ expression on T-cells. Functionally, SIRPγlow CD8 T-cells in CT and TT individuals existed in a heightened efector state with lower activation threshold and had greater expression of genes and molecules associated with migratory and cytotoxic potential. Further, SIRPγlow CD8 T-cells were defcient in transcription factors associated with long-term functional memory formation. Our study reveals biological consequences of the SNP rs2281808 and provides novel insights into the potential mechanisms by which SIRPγ might regulate human immune responses. Genome-wide association studies have been instrumental in identifying genetic risk variants in autoimmune diseases. However, in most cases, the biological interpretation of how the reported risk variants potentiate auto- immunity remains unknown. Multiple GWAS studies have shown that the SNP rs2281808 TT variant is associ- ated with type 1 diabetes (T1D)1–3.
    [Show full text]